Presenting answers from concept-based representation of a topic oriented pipeline

ABSTRACT

According to one exemplary embodiment, a method for generating an answer in a question answering system is provided. The method may include receiving a question. The method may also include identifying a candidate answer from a corpus. The method may then include determining a plurality of sentences based on the identified candidate answer. The method may further include calculating a similarity value for each sentence within the plurality of sentences based on comparing the plurality of sentences to the candidate answer and the received question. The method may also include identifying at least one sentence within the plurality of sentences with a calculated similarity value that exceeds a threshold value. The method may then include presenting the answer, whereby the answer comprises the plurality of sentences, the candidate answer, and metadata.

BACKGROUND

The present invention relates generally to the field of cognitivecomputing, and more particularly to topic oriented question answersystems.

When presented with a question, a topic oriented question answer systemidentifies the most relevant topics to that question from a corpus ofknowledge and returns the topics as candidate answers.

SUMMARY

According to one exemplary embodiment, a method for generating an answerin a question answering system is provided. The method may includereceiving a question. The method may also include identifying acandidate answer from a corpus. The method may then include determininga plurality of sentences based on the identified candidate answer. Themethod may further include calculating a similarity value for eachsentence within the plurality of sentences based on comparing theplurality of sentences to the candidate answer and the receivedquestion. The method may also include identifying at least one sentencewithin the plurality of sentences with a calculated similarity valuethat exceeds a threshold value. The method may then include presentingthe answer, whereby the answer comprises the plurality of sentences, thecandidate answer, and metadata.

According to another exemplary embodiment, a computer system forgenerating an answer in a question answering system is provided. Thecomputer system may include one or more processors, one or morecomputer-readable memories, one or more computer-readable tangiblestorage devices, and program instructions stored on at least one of theone or more storage devices for execution by at least one of the one ormore processors via at least one of the one or more memories, wherebythe computer system is capable of performing a method. The method mayinclude receiving a question. The method may also include identifying acandidate answer from a corpus. The method may then include determininga plurality of sentences based on the identified candidate answer. Themethod may further include calculating a similarity value for eachsentence within the plurality of sentences based on comparing theplurality of sentences to the candidate answer and the receivedquestion. The method may also include identifying at least one sentencewithin the plurality of sentences with a calculated similarity valuethat exceeds a threshold value. The method may then include presentingthe answer, whereby the answer comprises the plurality of sentences, thecandidate answer, and metadata.

According to yet another exemplary embodiment, a computer programproduct for generating an answer in a question answering system isprovided. The computer program product may include one or morecomputer-readable storage devices and program instructions stored on atleast one of the one or more tangible storage devices, the programinstructions executable by a processor. The computer program product mayinclude program instructions to receive a question. The computer programproduct may also include program instructions to identify a candidateanswer from a corpus. The computer program product may then includeprogram instructions to determine a plurality of sentences based on theidentified candidate answer. The computer program product may furtherinclude program instructions to calculate a similarity value for eachsentence within the plurality of sentences based on comparing theplurality of sentences to the candidate answer and the receivedquestion. The computer program product may also include programinstructions to identify at least one sentence within the plurality ofsentences with a calculated similarity value that exceeds a thresholdvalue. The computer program product may then include programinstructions to present the answer, whereby the answer comprises theplurality of sentences, the candidate answer, and metadata.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings. The various features of the drawings arenot to scale as the illustrations are for clarity in facilitating oneskilled in the art in understanding the invention in conjunction withthe detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIG. 2 is an operational flow chart illustrating a process for corpusingestion according to at least one embodiment;

FIG. 3 is an operational flow chart illustrating a process for answeringquestions according to at least one embodiment;

FIG. 4 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 5 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1, in accordance with anembodiment of the present disclosure; and

FIG. 6 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 5, in accordance with an embodiment of thepresent disclosure.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope of this invention to thoseskilled in the art. In the description, details of well-known featuresand techniques may be omitted to avoid unnecessarily obscuring thepresented embodiments.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The following described exemplary embodiments provide a system, methodand program product for generating answers in a topic oriented pipelineusing sentence similarity. As such, the present embodiment has thecapacity to improve the technical field of query answering with topicoriented pipelines by breaking down topics into sentences and combiningsimilar sentences to form concise answers. More specifically, asdocuments are ingested into the topic oriented pipeline and broken downinto topics, each topic may be further broken down into constituentsentences. Thereafter, the constituent sentences may be used as a basisto create a graph structure representing the sentences within the topic.Edges within the graph connecting nodes that correspond with constituentsentences may be assigned edge weights indicating sentence similaritybetween nodes (i.e., constituent sentences). Once a question is receivedby the topic oriented pipeline, candidate answers may be determined.Each candidate answer may then be analyzed to determine sentences thatare similar to the candidate answer based on the generated graph. Asubgraph may then be generated based on the candidate answer, similarsentences and the question. Then, a clique of sentences may bedetermined that includes sentences that may be similar to the questionand the clique of sentences may be returned as an answer.

Existing topic oriented pipelines execute by breaking a corpus of Ndocuments into K sub-documents (i.e., topics) based on the structure ofthe original documents whereby K is larger than N. In some instances, Kmay be much larger than N depending on the structure of the original Ndocuments. The structure of the document may be defined by the use offormatting tags, such as H tags (or headings in the case of .doc filesor fonts in the case of a .pdf file).

When presented with a question, the topic oriented pipeline identifiesthe most relevant topic(s) to that question and returns those identifiedtopic(s) as the answers. Topics may be returned as a whole passagecontaining the multiple sentences. By returning the complete topic, somesentences may be included that are not relevant and that may obfuscatethe answer to the question.

Therefore, it may be advantageous to, among other things, provide a wayto filter out sentences from the answers determined within a questionanswering system (i.e., topic oriented pipeline) that may besuperfluous.

According to at least one embodiment, ingested corpus (e.g., textdocuments) may be broken down into topics, whereby each topic is made upof multiple elements (i.e., sentences). Topics may then be furtherbroken down into M constituent sentences (i.e., sentences S₁, S₂, . . ., S_(M)). Then, a graph structure may be created corresponding to atopic that has M nodes corresponding to each of the M constituentsentences.

Thereafter, the edges connecting nodes in the graph may be assigned edgeweights based on comparing two sentences within the topic. According toat least one embodiment, edge weight may be determined based on if thetwo sentences appear in the same paragraph or if the two sentences arein two different paragraphs (i.e., the edge weight for an edge betweentwo sentences in the same paragraph may be higher than for two sentencesin different paragraphs). Similarly, edge weights may increase for edgesbetween sentences that are closer together than for sentences furtherapart. According to at least one other embodiment, sentences may beanalyzed to determine how strongly sentences are related using knownmethods, such as anaphora resolution. According to yet anotherembodiment, edge weight may be determined based on known methods forcalculating sentence similarity, such as ngram, entropy, etc. Then, thetopic oriented pipeline may continue to ingest corpus until presentedwith a question to answer.

According to at least one embodiment, once the topic oriented pipelinereceives an input question, candidate answers are determined fromingested corpus using known methods. A list of candidate answers (e.g.,individual sentences from within a topic) may be generated as candidateanswers are identified. Once a list of candidate answers has beendetermined, a candidate answer may be selected for further analysis.

Then, the graph generated during corpus ingestion may be traversed toidentify other constituent sentences within the topic that are stronglyconnected to the candidate answer sentence based on the edge weightsassigned previously. Thereafter, a new subgraph may be generatedincluding the candidate answer sentence and the strongly connectedsentences identified previously. Additionally, the input question may beadded to the subgraph and the edge weights from the input question tothe other sentences within the subgraph may be updated using knownsentence similarity measures as described previously.

Based on a predetermined threshold of similarity, a set of sentencesfrom the subgraph (i.e., a clique of sentences) may be identified whenan edge weight exceeds the threshold value. The candidate answer maythen be rescored using known methods to indicate the probability thatthe candidate answer and the remaining identified set of sentences maybe correct. Then, the set of sentences may be returned as an answer. Theremaining candidate answer sentences may then be iteratively analyzed ina similar way and returned as answers until all candidate answersentences have been analyzed.

Referring to FIG. 1, an exemplary networked computer environment 100 inaccordance with one embodiment is depicted. The networked computerenvironment 100 may include a computer 102 with a processor 104 and adata storage device 106 that is enabled to run a software program 108and a topic sentence representation program 110 a. The networkedcomputer environment 100 may also include a server 112 that is enabledto run a topic sentence representation program 110 b that may interactwith a database 114 and a communication network 116. The networkedcomputer environment 100 may include a plurality of computers 102 andservers 112, only one of which is shown. The communication network mayinclude various types of communication networks, such as a wide areanetwork (WAN), local area network (LAN), a telecommunication network, awireless network, a public switched network and/or a satellite network.It should be appreciated that FIG. 1 provides only an illustration ofone implementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environments may be made based on designand implementation requirements.

The client computer 102 may communicate with the server computer 112 viathe communications network 116. The communications network 116 mayinclude connections, such as wire, wireless communication links, orfiber optic cables. As will be discussed with reference to FIG. 4,server computer 112 may include internal components 902 a and externalcomponents 904 a, respectively, and client computer 102 may includeinternal components 902 b and external components 904 b, respectively.Server computer 112 may also operate in a cloud computing service model,such as Software as a Service (SaaS), Platform as a Service (PaaS), orInfrastructure as a Service (IaaS). Server 112 may also be located in acloud computing deployment model, such as a private cloud, communitycloud, public cloud, or hybrid cloud. Client computer 102 may be, forexample, a mobile device, a telephone, a personal digital assistant, anetbook, a laptop computer, a tablet computer, a desktop computer, orany type of computing devices capable of running a program, accessing anetwork, and accessing a database 114. According to variousimplementations of the present embodiment, the topic sentencerepresentation program 110 a, 110 b may interact with a database 114that may be embedded in various storage devices, such as, but notlimited to a computer/mobile device 102, a networked server 112, or acloud storage service.

According to the present embodiment, a user using a client computer 102or a server computer 112 may use the topic sentence representationprogram 110 a, 110 b (respectively) to answer questions associated witha topic oriented pipeline. The topic sentence representation method isexplained in more detail below with respect to FIGS. 2 and 3.

Referring now to FIG. 2, an operational flow chart illustrating theexemplary corpus ingestion process 200 by the topic sentencerepresentation program 110 a and 110 b (FIG. 1) according to at leastone embodiment is depicted.

At 202, a document (or other corpus) is received for ingesting into aquestion answering system, such as a topic oriented pipeline. Accordingto at least one embodiment, the document ingestion is handled usingknown topic oriented pipeline corpus ingestion methods.

Then, at 204, the received document is broken down into topics.According to at least one embodiment, the document's formatting may beused to identify topics (i.e., passages containing one or moresentences). The structure of the document may be defined by the use offormatting tags, such as H tags (or headings in the case of .doc filesor fonts in the case of a .pdf file). The sentences contained within aset of formatting tags may be identified as a topic. The document maythen be iteratively broken into component topics until the wholedocument has been divided into topics. Text passages making up eachtopic may be stored in a data repository, such as a database 114 (FIG.1).

Next, at 206, the topics determined at 204 are further broken down intoindividual sentences. According to at least one embodiment, using knownstring scanning methods, each sentence may be identified (e.g., scanningeach character to identify punctuation or other sentence ending and/orstarting indicators) and each sentence stored in a data structure, suchas an array. For example, topic T₁ may contain N sentences. Thus topicT₁ may be broken down into the individual sentences S₁, S₂, . . . S_(N).

At 208, a graph structure is generated based on the sentences identifiedat 206. According to at least one embodiment, each sentence associatedwith a topic may be used to populate a graph structure. For example, ifeach sentence identified at 206 was stored as an array element in asentence array, a graph node may be added for each array element(corresponding to a sentence). Thus, if there are 15 sentences in atopic, 15 array elements may be initialized, each containing a stringwith a sentence. Thereafter, nodes may be added to the graph having asentence string as the node value. Nodes may be iteratively added to thegraph until all array elements (e.g., 15) have been added as nodes, onenode corresponding to one sentence. Additionally, the graph structuregenerated may be a complete graph (i.e., every pair of nodes in thegraph is connected by a unique edge).

Then, at 210, edge weights are calculated and assigned to each edge inthe graph. According to at least one embodiment, edge weight may becalculated to indicate how similar sentences are to each other. Edgeweight calculation may be based on determining if the two sentencesappear in the same paragraph or if the two sentences are in twodifferent paragraphs (i.e., the edge weight for an edge between twosentences in the same paragraph may be higher than for two sentences indifferent paragraphs). Similarly, determined edge weights may increasefor edges between sentences that are closer together than for sentencesfurther apart. According to at least one other embodiment, sentences maybe analyzed to determine how strongly sentences are related using knownmethods, such as anaphora resolution. According to yet anotherembodiment, edge weight may be determined based on known methods forcalculating sentence similarity, such as ngram, entropy, etc. It may beappreciated that any single edge weight determination method may be usedas well as a combination of edge weight determination methods.

Once the edge weights have been calculated and assigned, the corpusingestion process 200 may handle any additional received corpusdocuments.

Referring now to FIG. 3, an operational flow chart illustrating theexemplary question answering process 300 by the topic sentencerepresentation program 110 a and 110 b (FIG. 1) according to at leastone embodiment is depicted.

At 302, an input question is received by the question answering system(e.g., topic oriented pipeline). According to at least one embodiment, atopic oriented pipeline may receive an input question to answer.

Next, at 304, the question answering system identifies candidate answersfrom previously ingested corpus. According to at least one embodiment,known methods are used by the topic oriented pipeline to identifycandidate answer sentences from within topics associated with ingestedcorpus (e.g., documents). The identified candidate answers may be storedas a list of entries having the candidate answer sentence along withother information, such as identifying the graph made previously at 208(FIG. 2) containing the candidate answer sentence.

Then, at 306, a candidate answer sentence is selected from theidentified candidate answers. According to at least one embodiment, thefirst unanalyzed entry in the list of identified candidate answers maybe selected for further analysis. The entry may be read to obtain thecandidate answer sentence along with the graph previously generated at208 (FIG. 2) containing the other sentences within the topic associatedwith the candidate answer graph.

At 308, sentences that are strongly connected to the selected candidateanswer are identified. According to at least one embodiment, the graphassociated with the selected candidate answer may be traversed using aknown graph traversal algorithm (e.g., depth-first search) to identifyother sentences within the graph that are strongly connected to thecandidate answer. Sentences may be identified as being stronglyconnected to the candidate answer sentence by comparing the edge weightassigned to the edge spanning between the node corresponding to thecandidate answer and another sentence to a threshold value. If, as thegraph is traversed, an edge from the node corresponding to the candidateanswer sentence to a second node exceeds the threshold value, thesentence associated with the second node may be identified as stronglyconnected to the candidate answer sentence.

For example, in a scenario that includes edge weights within the topicgraph containing the candidate answer sentence that have been normalizedusing a known normalization algorithm to be a value between 0.0 and 1.0,a predetermined threshold value of 0.75 may be specified. If an edgespanning between the node representing the candidate answer sentence anda second node has an edge weight of 0.86, the sentence associated withthe second node may be identified as a strongly connected sentence sincethe edge weight (i.e., 0.86) exceeds the threshold value (i.e., 0.75).

Sentences identified as being strongly connected to the candidate answersentence may then be added to and stored in a data structure, such as anarray.

Next, at 310, a candidate answer subgraph is generated based on thecandidate answer and the sentences strongly connected with the candidateanswer sentence. According to at least one embodiment, a new graphstructure may be initialized and have nodes added corresponding to thecandidate answer sentence and the sentences identified as being stronglyconnected to the candidate answer sentence as described previously at308. Additionally, the nodes in the subgraph may be connected by edgessuch that a complete graph is formed.

According to at least one embodiment, the subgraph's edges may beassigned edge weights that are the same edge weights that existed in theoriginal graph generated previously at 208 (FIG. 2) between the noderepresenting the candidate answer sentence and the nodes representingstrongly connected sentences.

For example, an input question “What are the locations that IBM hasoffices in North Carolina?” may be received by a topic orientedpipeline. Based on searching the corpus, a candidate answer sentence“IBM is located in Raleigh, North Carolina” (i.e., S₁) may beidentified. Based on the candidate answer, sentence S₂ “There are 700employees at the Raleigh location,” sentence S₃ “There is a huge IBMfacility in Charlotte, North Carolina,” and sentence S₄ “There was arecent management change at the IBM Charlotte office” may be identifiedas being strongly connected to the candidate answer, S₁. From theidentified sentences (i.e., S₁-S₄), a subgraph may be generated withfour nodes: N₁ corresponding with the candidate answer S₁, N₂corresponding with sentence S₂, N₃ corresponding with sentence S₃, andN₄ corresponding with sentence S₄. Furthermore, an edge may be generatedto connect each node pair in the graph (e.g., edge E₁₂ connecting N₁ toN₂, edge E₂₃ connecting N₂ to N₃, etc.). Additionally, metadataassociated with each candidate answer sentence may be recorded, such aswhich paragraph and document contained the sentence.

Then, at 312, the input question is added as a node to the previouslygenerated subgraph. According to at least one embodiment, a nodecorresponding to the input question may be added to the subgraph using aknown graph node insertion algorithm. Additionally, the added node mayhave a unique edge generated between the added node and each of theexisting nodes in the subgraph.

Continuing the above example of the subgraph with nodes N₁-N₄, the inputquestion (i.e., “What are the locations that IBM has offices in NorthCarolina?”) may be added to the subgraph as node N₅. Additionally, edgesmay be added from N₅ to the rest of the nodes in the subgraph (N₁-N₄).Thus, the subgraph would contain nodes N₁-N₅ and all nodes may beconnected by a unique edge.

At 314, edge weights for edges within the subgraph are updated based onthe previously inserted node corresponding to the input question.According to at least one embodiment, the edge weights for the new edgesspanning from the new node (i.e., node corresponding with the inputquestion) to the existing subgraph nodes are assigned edge weights usingknown sentence similarity algorithms comparing the input question withthe sentences represented in the rest of the subgraph (i.e., thecandidate answer sentence and the sentences that were identified asbeing strongly connected to the candidate answer), as describedpreviously.

For example, in the subgraph having nodes N₁-N₅, edge weights may becalculated for the edges spanning between the added node (i.e., N₅) andthe other nodes (i.e., N₁-N₄). Using known similarity measures, theinput question may be compared with another sentence. For example, theinput question (i.e., “What are the locations that IBM has offices inNorth Carolina?”) may be compared with sentence S₁ (i.e., “IBM islocated in Raleigh, North Carolina”) and a normalized similarity measureof 0.98 may be generated since the two sentences are closely related.Thus, the edge (i.e., E₅₁) between the input question node (i.e., N₅)and the node corresponding to sentence S₁ (i.e., N₁) may be assigned anedge weight of 0.98. Then, the input question may be compared with thesentence S₂ (i.e., “There are 700 employees at the Raleigh location.”)and result in a normalized similarity value of 0.61 since the twosentences are not closely related. The edge (i.e., E₅₂) between theinput question node and the node corresponding to sentence S₂ (i.e., N₂)may thus be assigned an edge weight of 0.61. Next, the input questionmay be compared with the sentence S₃ (i.e., “There is a huge IBMfacility in Charlotte, North Carolina.”) and result in a normalizedsimilarity value of 0.97 since the two sentences are closely related.The edge (i.e., E₅₃) between the input question node and the nodecorresponding to sentence S₃ (i.e., N₃) may thus be assigned an edgeweight of 0.97. Finally, the input question may be compared with thesentence S₄ (i.e., “There was a recent management change at the IBMCharlotte office.”) and result in a normalized similarity value of 0.59since the two sentences are not closely related. The edge (i.e., E₅₄)between the input question node and the node corresponding to sentenceS₄ (i.e., N₄) may thus be assigned an edge weight of 0.59.

It may be appreciated that other ways of updating the edge weights maybe implemented, such as a hierarchal edge weighting scheme. For example,a hierarchal edge weighting scheme may identify a first node that isstrongly connected to the question. Then an edge connecting the firstnode to a second node may be assigned an edge weight based on thesimilarity between the second node and the question combined with thefirst node. The second node may then be added to the first node andquestion to be compared with a third node, etc.

Next, at 316, a clique of sentences (i.e., set of sentences) isidentified within the subgraph. According to at least one embodiment,the subgraph may be traversed using a known graph traversal algorithm(e.g., depth-first search) to identify sentences within the subgraphthat are similar to the input question and sentences that may be similarto each other. Sentences may be identified as being similar by comparingthe edge weight assigned to the edge spanning between two nodes in thesubgraph to a predetermined similarity threshold value.

Edges within the subgraph may be analyzed to determine similaritybetween the added node corresponding to the input question and othernodes (i.e., edges spanning between the input question node and othernodes), as well as determine similarity the other nodes have betweeneach other (e.g., edges that are not connected to the input questionnode). If, as the subgraph is traversed, an edge from a first node(e.g., corresponding to the input question) to a second node (e.g.,corresponding to another sentence in the subgraph) exceeds the thresholdvalue, the sentences corresponding to the first and second nodes may beadded to the clique of sentences. Sentences added to the clique ofsentences may be stored in a data structure, such as a list.

Continuing the example subgraph described previously having nodes N₁-N₅,edge weights associated with edges (i.e., E₅₁, E₅₂, E₅₃, and E₅₄) fromthe input question (i.e., N₅) to the rest of the nodes (i.e., N₁-N₄) maybe iteratively compared against a predetermined threshold similarityvalue (e.g., 0.80).

First, edge E₅₁ having an edge weight of 0.98 may be compared againstthe threshold value of 0.80. Since the edge weight of E₅₁ exceeds thethreshold value of 0.80, the sentence S₁ corresponding to N₁ isidentified as part of the clique of sentences and added to a datastructure, such as an array.

Next, edge E₅₂ having an edge weight of 0.61 may be compared against thethreshold value of 0.80. Since the edge weight of E₅₂ does not exceedthe threshold value of 0.80, the sentence S₂ corresponding to N₂ isidentified as not being part of the clique of sentences and would not beadded to the array containing the clique of sentences.

Then, edge E₅₃ having an edge weight of 0.97 may be compared against thethreshold value of 0.80. Since the edge weight of E₅₃ exceeds thethreshold value of 0.80, the sentence S₃ corresponding to N₃ isidentified as part of the clique of sentences and added to the arraycontaining the clique of sentences.

Finally, edge E₅₄ having an edge weight of 0.59 may be compared againstthe threshold value of 0.80. Since the edge weight of E₅₄ does notexceed the threshold value of 0.80, the sentence S₄ corresponding to N₄is identified as not being part of the clique of sentences and would notbe added to the array containing the clique of sentences. Thus, finalclique of sentences may include sentences S₁ and S₃.

Then, at 318, the clique of sentences (i.e., candidate answer sentenceand other sentences identified at 316) is returned along with associatedmetadata as an answer. According to at least one embodiment, themetadata associated with the clique of sentences may include the edgeweights of edges from the node representing the input question to thenodes corresponding to each sentence within the clique of sentencesidentified at 316, or which document the sentence was found in and wherein the document the sentences was found (e.g., paragraph 3), or otherdata that may be used to determine if a sentence is related to the inputquestion.

For example, the input question may be “What are the locations that IBMhas offices in North Carolina?” After analysis, three sentences may beadded to the clique of sentences as the answer (e.g., “IBM has a campusin Research Triangle Park,” “IBM has a campus in Charlotte,” and “IBMhas a lab in Cary”). Furthermore, metadata associated with the threesentences within the clique of sentences may be included as part of theanswer. For instance, metadata corresponding to the first sentence “IBMhas a campus in Research Triangle Park” may include that the sentenceappears in paragraph two (i.e., paragraph identifier) of document three(i.e., document identifier). Metadata corresponding to the secondsentence “IBM has a campus in Charlotte” may include the sentenceappears in paragraph four of document five. Finally, metadatacorresponding to the third sentence “IBM has a lab in Cary” may includea similarity score of 89%, describing the similarity of the sentence tothe input question as determined previously at 314.

According to at least one other embodiment, the clique of sentences maybe returned as the answer without additional metadata.

Next, at 320, it is determined if all candidate answers have beenanalyzed. According to at least one embodiment, the list of candidateanswers may be queried to determine if there are any entries that havenot been analyzed. Additionally, the clique of answers returned at 318may be stored in a data repository, such as a database 114 (FIG. 1) forlater retrieval pending any more cliques of sentences being found insubsequent iterations.

If it is determined that there is a candidate answer that has not beenanalyzed at 320, the question answering process 300 will return to 306to analyze the next unanalyzed candidate answer.

However, if it is determined that all candidate answer have beenanalyzed at 320, the previously returned answers (e.g., cliques ofsentences and associated metadata) are presented to an entity, such as auser that asked the input question, at 322. According to at least oneembodiment, the answers may be presented by writing the answers to anoutput data file (e.g., a text file), a graphical user interface (GUI),etc.

Continuing the previous example of the clique of sentences andassociated metadata, the presented answers may be displayed to a userusing a GUI that shows the three sentences from the clique of sentencesalong with the metadata (i.e., the paragraph and document the sentencewas found and one or more similarity scores) associated with thesentences. Thus the GUI may output the first sentence and metadata as“S1: IBM has a campus in Research Triangle Park (paragraph 2 of document3),” the second sentence as “S2: IBM has a campus in Charlotte(paragraph 4 of document 5),” and the third sentence as “S3: IBM has acampus in Cary (similarity score=89%).”

The question answering process 300 may then end and the questionanswering system (e.g., topic oriented pipeline) may proceed to performother actions incident to generating answers to the input question.

It may be appreciated that FIGS. 2 and 3 provide only an illustration ofone embodiment and does not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted embodiment(s) may be made based on design and implementationrequirements.

FIG. 4 is a block diagram 900 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment of the present invention. It should be appreciated that FIG.4 provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironments may be made based on design and implementationrequirements.

Data processing system 902, 904 is representative of any electronicdevice capable of executing machine-readable program instructions. Dataprocessing system 902, 904 may be representative of a smart phone, acomputer system, PDA, or other electronic devices. Examples of computingsystems, environments, and/or configurations that may represented bydata processing system 902, 904 include, but are not limited to,personal computer systems, server computer systems, thin clients, thickclients, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, network PCs, minicomputer systems, anddistributed cloud computing environments that include any of the abovesystems or devices.

User client computer 102 (FIG. 1), and network server 112 (FIG. 1) mayinclude respective sets of internal components 902 a, b and externalcomponents 904 a, b illustrated in FIG. 4. Each of the sets of internalcomponents 902 a, b includes one or more processors 906, one or morecomputer-readable RAMs 908 and one or more computer-readable ROMs 910 onone or more buses 912, and one or more operating systems 914 and one ormore computer-readable tangible storage devices 916. The one or moreoperating systems 914 and the software program 108 (FIG. 1) and thetopic sentence representation program 110 a (FIG. 1) in client computer102 (FIG. 1) and the topic sentence representation program 110 b(FIG. 1) in network server 112 (FIG. 1), may be stored on one or morecomputer-readable tangible storage devices 916 for execution by one ormore processors 906 via one or more RAMs 908 (which typically includecache memory). In the embodiment illustrated in FIG. 4, each of thecomputer-readable tangible storage devices 916 is a magnetic diskstorage device of an internal hard drive. Alternatively, each of thecomputer-readable tangible storage devices 916 is a semiconductorstorage device such as ROM 910, EPROM, flash memory or any othercomputer-readable tangible storage device that can store a computerprogram and digital information.

Each set of internal components 902 a, b also includes a R/W drive orinterface 918 to read from and write to one or more portablecomputer-readable tangible storage devices 920 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 (FIG. 1) and the topic sentence representation program 110 aand 110 b (FIG. 1) can be stored on one or more of the respectiveportable computer-readable tangible storage devices 920, read via therespective R/W drive or interface 918 and loaded into the respectivehard drive 916.

Each set of internal components 902 a, b may also include networkadapters (or switch port cards) or interfaces 922 such as a TCP/IPadapter cards, wireless wi-fi interface cards, or 3G or 4G wirelessinterface cards or other wired or wireless communication links. Thesoftware program 108 (FIG. 1) and the topic sentence representationprogram 110 a (FIG. 1) in client computer 102 (FIG. 1) and the topicsentence representation program 110 b (FIG. 1) in network servercomputer 112 (FIG. 1) can be downloaded from an external computer (e.g.,server) via a network (for example, the Internet, a local area networkor other, wide area network) and respective network adapters orinterfaces 922. From the network adapters (or switch port adaptors) orinterfaces 922, the software program 108 (FIG. 1) and the topic sentencerepresentation program 110 a (FIG. 1) in client computer 102 (FIG. 1)and the topic sentence representation program 110 b (FIG. 1) in networkserver computer 112 (FIG. 1) are loaded into the respective hard drive916. The network may comprise copper wires, optical fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers.

Each of the sets of external components 904 a, b can include a computerdisplay monitor 924, a keyboard 926, and a computer mouse 928. Externalcomponents 904 a, b can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 902 a, b also includes device drivers930 to interface to computer display monitor 924, keyboard 926 andcomputer mouse 928. The device drivers 930, R/W drive or interface 918and network adapter or interface 922 comprise hardware and software(stored in storage device 916 and/or ROM 910).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 5, illustrative cloud computing environment 1000is depicted. As shown, cloud computing environment 1000 comprises one ormore cloud computing nodes 100 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 1000A, desktop computer 1000B, laptopcomputer 1000C, and/or automobile computer system 1000N may communicate.Nodes 100 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 1000to offer infrastructure, platforms and/or software as services for whicha cloud consumer does not need to maintain resources on a localcomputing device. It is understood that the types of computing devices1000A-N shown in FIG. 5 are intended to be illustrative only and thatcomputing nodes 100 and cloud computing environment 1000 can communicatewith any type of computerized device over any type of network and/ornetwork addressable connection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers 1100provided by cloud computing environment 1000 (FIG. 5) is shown. Itshould be understood in advance that the components, layers, andfunctions shown in FIG. 6 are intended to be illustrative only andembodiments of the invention are not limited thereto. As depicted, thefollowing layers and corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and topic sentence representation 96. A topicsentence representation program 110 a, 110 b (FIG. 1) provides a way torepresent sentences within a topic in a graph and determine sentencesimilarity to an input question, whereby sentences having sufficientsimilarity to the input question may be returned as an answer to a topicoriented pipeline.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method for generating an answer in a questionanswering system, the method comprising: receiving a question;identifying a candidate answer from a corpus; determining a plurality ofsentences based on the identified candidate answer; calculating asimilarity value for each sentence within the plurality of sentencesbased on comparing the plurality of sentences to the candidate answerand the received question; generating a subgraph based on the pluralityof sentences and the candidate answer, wherein the subgraph has aplurality of edges and a plurality of nodes corresponding to theplurality of sentences and the candidate answer; adding a first node tothe subgraph corresponding to the received question; assigning an edgeweight to each edge within the plurality of edges based on thecalculated similarity value for each sentence within the plurality ofsentences; determining metadata associated with each sentence within theplurality of sentences, wherein the metadata includes a paragraphidentifier and a document identifier; identifying at least one sentencewithin the plurality of sentences based on determining the assigned edgeweight of the identified at least one sentence exceeds a thresholdvalue; and presenting the answer, wherein the answer comprises theplurality of sentences, the candidate answer, and the determinedmetadata.
 2. The method of claim 1, wherein the metadata comprises atleast one of a paragraph identifier, a document identifier, and thesimilarity value.
 3. The method of claim 1, wherein presenting theanswer comprises at least one of writing the answer to a data file anddisplaying the answer in a graphical user interface (GUI).
 4. The methodof claim 1, wherein adding the first node to the subgraph comprisesadding a plurality of first node edges to the subgraph, wherein eachfirst node edge within the plurality of first node edges connects thefirst node to each node within the plurality of nodes.
 5. The method ofclaim 1, further comprising: receiving a document; determining aplurality of topics based on the received document; determining aplurality of topic sentences for each topic within the plurality oftopics; generating a topic graph based on the determined plurality oftopic sentences, wherein the topic graph includes a plurality of topicedges; and calculating a plurality of topic edge weights correspondingto the plurality of topic edges based on the similarity of a pair oftopic sentences within the plurality of topic sentences.
 6. A computersystem for generating an answer in a question answering system,comprising: one or more processors, one or more computer-readablememories, one or more computer-readable tangible storage medium, andprogram instructions stored on at least one of the one or more tangiblestorage medium for execution by at least one of the one or moreprocessors via at least one of the one or more memories, wherein thecomputer system is capable of performing a method comprising: receivinga question; identifying a candidate answer from a corpus; determining aplurality of sentences based on the identified candidate answer;calculating a similarity value for each sentence within the plurality ofsentences based on comparing the plurality of sentences to the candidateanswer and the received question; generating a subgraph based on theplurality of sentences and the candidate answer, wherein the subgraphhas a plurality of edges and a plurality of nodes corresponding to theplurality of sentences and the candidate answer; adding a first node tothe subgraph corresponding to the received question; assigning an edgeweight to each edge within the plurality of edges based on thecalculated similarity value for each sentence within the plurality ofsentences; determining metadata associated with each sentence within theplurality of sentences, wherein the metadata includes a paragraphidentifier and a document identifier; identifying at least one sentencewithin the plurality of sentences based on determining the assigned edgeweight of the identified at least one sentence exceeds a thresholdvalue; and presenting the answer, wherein the answer comprises theplurality of sentences, the candidate answer, and the determinedmetadata.
 7. The computer system of claim 6, wherein the metadatacomprises at least one of a paragraph identifier, a document identifier,and the similarity value.
 8. The computer system of claim 6, whereinpresenting the answer comprises at least one of writing the answer to adata file and displaying the answer in a graphical user interface (GUI).9. The computer system of claim 6, wherein adding the first node to thesubgraph comprises adding a plurality of first node edges to thesubgraph, wherein each first node edge within the plurality of firstnode edges connects the first node to each node within the plurality ofnodes.
 10. The computer system of claim 6, further comprising: receivinga document; determining a plurality of topics based on the receiveddocument; determining a plurality of topic sentences for each topicwithin the plurality of topics; generating a topic graph based on thedetermined plurality of topic sentences, wherein the topic graphincludes a plurality of topic edges; and calculating a plurality oftopic edge weights corresponding to the plurality of topic edges basedon the similarity of a pair of topic sentences within the plurality oftopic sentences.
 11. A computer program product for generating an answerin a question answering system, comprising: one or morecomputer-readable storage medium and program instructions stored on atleast one of the one or more tangible storage medium, the programinstructions executable by a processor, the program instructionscomprising: program instructions to receive a question; programinstructions to identify a candidate answer from a corpus; programinstructions to determine a plurality of sentences based on theidentified candidate answer; program instructions to calculate asimilarity value for each sentence within the plurality of sentencesbased on comparing the plurality of sentences to the candidate answerand the received question; program instructions to generate a subgraphbased on the plurality of sentences and the candidate answer, whereinthe subgraph has a plurality of edges and a plurality of nodescorresponding to the plurality of sentences and the candidate answer;program instructions to add a first node to the subgraph correspondingto the received question; program instructions to assign an edge weightto each edge within the plurality of edges based on the calculatedsimilarity value for each sentence within the plurality of sentences;program instructions to determine metadata associated with each sentencewithin the plurality of sentences, wherein the metadata includes aparagraph identifier and a document identifier; program instructions toidentify at least one sentence within the plurality of sentences basedon determining the assigned edge weight of the identified at least onesentence exceeds a threshold value; and program instructions to presentthe answer, wherein the answer comprises the plurality of sentences, thecandidate answer, and the determined metadata.
 12. The computer programproduct of claim 11, wherein the metadata comprises at least one of aparagraph identifier, a document identifier, and the similarity value.13. The computer program product of claim 11, wherein presenting theanswer comprises at least one of writing the answer to a data file anddisplaying the answer in a graphical user interface (GUI).
 14. Thecomputer program product of claim 11, wherein adding the first node tothe subgraph comprises adding a plurality of first node edges to thesubgraph, wherein each first node edge within the plurality of firstnode edges connects the first node to each node within the plurality ofnodes.